Online Combinatorial Optimization with Group Fairness Constraints
Authors: Negin Golrezaei, Rad Niazadeh, Kumar Kshitij Patel, Fransisca Susan
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide theoretical guarantees for our method and empirically evaluate it, demonstrating its effectiveness. We present an empirical evaluation of our algorithm using parameters derived from the widely used Movie Lens ratings dataset [Harper and Konstan, 2015]. Our results demonstrate that our online algorithm achieves sub-linear regret when comparing it to the approximate optimal benchmark. |
| Researcher Affiliation | Academia | 1Sloan School of Management, MIT 2Booth School of Business, University of Chicago 3Toyota Technological Institute at Chicago (TTIC) |
| Pseudocode | Yes | Algorithm 1 Robust Online Algorithm |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the described methodology. The link provided in the introduction is for the full version of the paper on SSRN (https://ssrn.com/abstract=4824251). |
| Open Datasets | Yes | In this section, we present a case study using the Movie Lens 1M dataset due to [Harper and Konstan, 2015] to evaluate the effectiveness of our offline (c.f., our full version for more details) and online algorithms for movie recommendation. |
| Dataset Splits | No | The paper mentions using the Movie Lens 1M dataset and discusses experimental runs, but it does not specify explicit train/validation/test splits (e.g., percentages, sample counts, or predefined citations for splits) for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments, only mentioning general computing concepts. |
| Software Dependencies | No | The paper mentions software components like "projected OGD", "Multiplicative Weight/Hedge algorithm", and frameworks by cited authors ([Nemhauser et al., 1978], [Niazadeh et al., 2021]), but it does not provide specific version numbers for any of these or other key software dependencies. |
| Experiment Setup | Yes | In the experiment, we consider three sets of thresholds for the average market share for each group: (0.5, 0.5), (0.6, 0.6), and (0.7, 0.7), where all of these thresholds are feasible. We set the maximum allowed violation δ to be 0.01. Moreover, we set the number of periods to be T = 10, 000 and the allowed violation to be δ = 0.01 for both settings. |